SwissMTB: establishing comprehensive molecular cancer diagnostics in Swiss clinics

Circulating Tumor Reactive KIR+CD8+ T cells Suppress Anti-Tumor Immunity in Patients with Melanoma

Effective anti-tumor immunity is largely driven by cytotoxic CD8+ T cells that can specifically recognize tumor antigens. However, the factors which ultimately dictate successful tumor rejection remain poorly understood. Here we identify a subpopulation of CD8+ T cells which are tumor antigen-specific in patients with melanoma but resemble KIR+CD8+ T cells with a regulatory function (Tregs). These tumor antigen-specific KIR+CD8+ T cells are detectable in both the tumor and the blood, and higher levels of this population are associated with worse overall survival. Our findings therefore suggest that KIR+CD8+ Tregs are tumor antigen-specific but uniquely suppress anti-tumor immunity in patients with melanoma.

AMBAR - Interactive Alteration annotations for molecular tumor boards

MOTIVATION

Personalized decision-making for cancer therapy relies on molecular profiling from sequencing data in combination with database evidence and expert knowledge. Molecular tumor boards (MTBs) bring together clinicians and scientists with diverse expertise and are increasingly established in the clinical routine for therapeutic interventions. However, the analysis and documentation of patients data are still time-consuming and difficult to manage for MTBs, especially as few tools are available for the amount of information required.

RESULTS

To overcome these limitations, we developed an interactive web application AMBAR (Alteration annotations for Molecular tumor BoARds), for therapeutic decision-making support in MTBs. AMBAR is an R shiny-based application that allows customization, interactive filtering, visualization, adding expert knowledge, and export to clinical systems of annotated mutations.

AVAILABILITY

AMBAR is dockerized, open source and available at https://sysbio.uni-ulm.de/?Software:Ambar Contact:hans.kestler@uni-ulm.de.

MTPpilot: An Interactive Software for Visualization of Next-Generation Sequencing Results in Molecular Tumor Boards

Comprehensive targeted next-generation sequencing (NGS) panels are routinely used in modern molecular cancer diagnostics. In molecular tumor boards, the detected genomic alterations are often discussed to decide the next treatment options for patients with cancer. With the increasing size and complexity of NGS panels, the discussion of these results becomes increasingly complex, especially if they are reported in a text-based form, as it is the standard in current molecular pathology.

scAmpi—A versatile pipeline for single-cell RNA-seq analysis from basics to clinics

Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful technique to decipher tissue composition at the single-cell level and to inform on disease mechanisms, tumor heterogeneity, and the state of the immune microenvironment. Although multiple methods for the computational analysis of scRNA-seq data exist, their application in a clinical setting demands standardized and reproducible workflows, targeted to extract, condense, and display the clinically relevant information. To this end, we designed scAmpi (Single Cell Analysis mRNA pipeline), a workflow that facilitates scRNA-seq analysis from raw read processing to informing on sample composition, clinically relevant gene and pathway alterations, and in silico identification of personalized candidate drug treatments. We demonstrate the value of this workflow for clinical decision making in a molecular tumor board as part of a clinical study.

Single-cell RNA sequencing (scRNA-seq) measures the expression levels across the genes expressed in each single cell. Thus, it is well suited to inform on the cell type composition and the function of cells in different tissues and diseases. However, it is challenging to correctly process and interpret the large amounts of data generated with scRNA-seq. To this end, we have developed an analysis workflow named scAmpi (Single Cell Analysis mRNA pipeline) that starts on the raw sequencing data and performs preprocessing, quality control, and subsequent analysis steps following state-of-the-art recommendations for scRNA-seq processing. The workflow removes low quality cells, assigns a cell type label to each cell, and visualizes the expression of individual genes of interest and functional pathways on the single cells. Moreover, in disease-related analyses scAmpi can link the observed gene expression to potential drug candidates that could be suited to treat the disease.

Development and validation of ACTE-MTB: A tool to systematically assess the maturity of molecular tumor boards

Molecular tumor boards (MTBs) require specialized activities to leverage genomic data for therapeutic decision-making. Currently, there are no defined standards for implementing, executing, and tracking the impact of MTBs. This study describes the development and validation of ACTE-MTB, a tool to evaluate the maturity of an organization’s MTB to identify specific areas that would benefit from process improvements and standardization. The ACTE-MTB maturity assessment tool is composed of 3 elements: 1) The ACTE-MTB maturity model; 2) a 59-question survey on MTB processes and challenges; and 3) a 5-level MTB maturity scoring algorithm. This tool was developed to measure MTB maturity in the categories of Access, Consultation, Technology, and Evidence (ACTE) and was tested on 20 MTBs spanning the United States, Europe, and Asia-Pacific regions. Validity testing revealed that the average maturity score was 3.3 out of 5 (+/- 0.1; range 2.0–4.3) with MTBs in academic institutions showing significantly higher overall maturity levels than in non-academic institutions (3.7 +/- 0.2 vs. 3.1 +/- 0.2; P = .018). While maturity scores for academic institutions were higher for Consultation, Technology, and Evidence domains, the maturity score for the Access domain did not significantly differ between the two groups, highlighting a disconnect between MTB operations and the downstream impact on ability to access testing and/or therapies. To our knowledge, ACTE-MTB is the first tool of its kind to enable structured, maturity assessment of MTBs in a universally-applicable manner. In the process of establishing construct validity of this tool, opportunities for further investigation and improvements were identified that address the key functional areas of MTBs that would likely benefit from standardization and best practice recommendations. We believe a unified approach to assessment of MTB maturity will help to identify areas for improvement at both the organizational and system level.

Variomes: a high recall search engine to support the curation of genomic variants

Motivation

Identification and interpretation of clinically actionable variants is a critical bottleneck. Searching for evidence in the literature is mandatory according to ASCO/AMP/CAP practice guidelines; however, it is both labor-intensive and error-prone. We developed a system to perform triage of publications relevant to support an evidence-based decision. The system is also able to prioritize variants. Our system searches within pre-annotated collections such as MEDLINE and PubMed Central.

Results

We assess the search effectiveness of the system using three different experimental settings: literature triage; variant prioritization and comparison of Variomes with LitVar. Almost two-thirds of the publications returned in the top-5 are relevant for clinical decision-support. Our approach enabled identifying 81.8% of clinically actionable variants in the top-3. Variomes retrieves on average +21.3% more articles than LitVar and returns the same number of results or more results than LitVar for 90% of the queries when tested on a set of 803 queries; thus, establishing a new baseline for searching the literature about variants.

Availability and implementation

Variomes is publicly available at https://candy.hesge.ch/Variomes. Source code is freely available at https://github.com/variomes/sibtm-variomes. SynVar is publicly available at https://goldorak.hesge.ch/synvar.

Supplementary information

Supplementary data are available at Bioinformatics online.

Knowledge bases and software support for variant interpretation in precision oncology

Precision oncology is a rapidly evolving interdisciplinary medical specialty. Comprehensive cancer panels are becoming increasingly available at pathology departments worldwide, creating the urgent need for scalable cancer variant annotation and molecularly informed treatment recommendations. A wealth of mainly academia-driven knowledge bases calls for software tools supporting the multi-step diagnostic process. We derive a comprehensive list of knowledge bases relevant for variant interpretation by a review of existing literature followed by a survey among medical experts from university hospitals in Germany. In addition, we review cancer variant interpretation tools, which integrate multiple knowledge bases. We categorize the knowledge bases along the diagnostic process in precision oncology and analyze programmatic access options as well as the integration of knowledge bases into software tools. The most commonly used knowledge bases provide good programmatic access options and have been integrated into a range of software tools. For the wider set of knowledge bases, access options vary across different parts of the diagnostic process. Programmatic access is limited for information regarding clinical classifications of variants and for therapy recommendations. The main issue for databases used for biological classification of pathogenic variants and pathway context information is the lack of standardized interfaces. There is no single cancer variant interpretation tool that integrates all identified knowledge bases. Specialized tools are available and need to be further developed for different steps in the diagnostic process.

[Molecular tumor boards - insights and perspectives]

The rapid development of molecular technologies and targeted therapies has fostered the implementation of specialized tumor conferences, known as molecular tumor boards (MTBs). MTBs become particularly important when treatment recommendations are needed based on molecular alterations beyond the approved targeted therapies. While an MTB's goals are based on individualized diagnostics and therapies of tumor patients using innovative technologies and biomarkers, the procedures of MTBs are still quite heterogeneous. This applies to the primary inclusion criteria for tumor patients, the composition of MTBs, the applied diagnostic tests and their assessment and reporting, the evaluation of their clinical value and implementation in a therapeutic strategy, and the associated quality assurance measurements as well as knowledge-gaining, economical, legal, and ethical aspects.This article provides an overview of the spectrum of MTBs, their challenges, and the potential for individualized cancer medicine.

Pathologists at the Leading Edge of Optimizing the Tumor Tissue Journey for Diagnostic Accuracy and Molecular Testing

OBJECTIVES

Tumor biomarker analyses accompanying immuno-oncology therapies are coupled with a tumor tissue journey aiming to guide tissue procurement and allow for accurate diagnosis and delivery of test results. The engagement of pathologists in the tumor tissue journey is essential because they are able to link the preanalytic requirements of this process with pathologic evaluation and clinical information, ultimately influencing treatment decisions for patients with cancer. The aim of this review is to provide suggestions on how cancer diagnosis and the delivery of molecular test results may be optimized, based on the needs and available resources of institutions, by placing the tumor tissue journey under the leadership of pathologists.

METHODS

Literature searches on PubMed and personal experience provided the necessary material to satisfy the objectives of this review.

RESULTS

Pathologists are usually involved across many steps of the tumor tissue journey and have the requisite knowledge to ensure its efficiency.

CONCLUSIONS

The expansion of oncology diagnostic testing emphasizes the need for pathologists to acquire a leadership role in the multidisciplinary effort to optimize the accuracy, completeness, and delivery of diagnoses guiding personalized treatments.

A showcase study on personalized in silico drug response prediction based on the genetic landscape of muscle invasive bladder cancer

Improved and cheaper molecular diagnostics allow the shift from "one size fits all" therapies to personalised treatments targeting the individual tumor. However, the wealth of potential targets based on comprehensive sequencing remains a yet unsolved challenge that prevents its routine use in clinical practice. Thus, we designed a workflow that selects the most promising treatment targets based on multi-omics sequencing and in silico drug prediction. In this study we demonstrate the workflow with focus on bladder cancer (BLCA), as there are, to date, no reliable diagnostics available to predict the potential benefit of a therapeutic approach. Within the TCGA-BLCA cohort, our workflow identified a panel of 21 genes and 72 drugs that suggested personalized treatment for 95% of patients-including five genes not yet reported as prognostic markers for clinical testing in BLCA. The automated predictions were complemented by manually curated data, thus allowing for accurate sensitivity- or resistance-directed drug response predictions. We discuss potential improvements of drug-gene interaction databases on the basis of pitfalls that were identified during manual curation.

From Somatic Variants Toward Precision Oncology: An Investigation of Reporting Practice for Next-Generation Sequencing-Based Circulating Tumor DNA Analysis

BACKGROUND

With the accelerated development of next-generation sequencing (NGS), identified variants, and targeted therapies, clinicians who confront the complicated and multifarious genetic information may not effectively incorporate NGS-based circulating tumor DNA (ctDNA) analysis into routine patient care. Consequently, standardized ctDNA testing reports are of vital importance. In an effort to guarantee high-quality reporting performance, we conducted an investigation of the current detection and reporting practices for NGS-based ctDNA analysis.

MATERIALS AND METHODS

A set of simulated ctDNA samples with known variants at known allelic frequencies and a corresponding case scenario were distributed to 66 genetic testing laboratories for ctDNA analysis. Written reports were collected to evaluate the detection accuracy, reporting integrity, and information sufficiency using 21 predefined criteria.

RESULTS

Current reporting practices for NGS-based ctDNA analysis were found to be far from satisfactory, especially regarding testing interpretation and methodological details. Only 42.4% of laboratories reported the results in complete concordance with the expected results. Moreover, 74.2% of reports only listed aberrations with direct and well-known treatment consequences for the tumor type in question. Genetic aberrations for which experimental agents and/or drug access programs are available may thus be overlooked. Furthermore, methodological details for the interpretation of results were missing from the majority of reports (87.9%).

CONCLUSION

This proof-of-principle study suggests that the capacity for accurate identification of variants, rational interpretation of genotypes, comprehensive recommendation of potential medications, and detailed description of methodologies need to be further improved before ctDNA analysis can be formally implemented in the clinic.

IMPLICATIONS FOR PRACTICE

Accurate, comprehensive, and standardized clinical sequencing reports can help to translate complex genetic information into patient-centered clinical decisions, thereby shepherding precision oncology into daily practice. However, standards, guidelines, and quality requirements for clinical reports of next-generation sequencing (NGS)-based circulating tumor DNA (ctDNA) analysis are currently absent. By using a set of simulated clinical ctDNA samples and a corresponding case scenario, current practices were evaluated to identify deficiencies in clinical sequencing reports of ctDNA analysis. The recommendations provided here may serve as a roadmap for the improved implementation of NGS-based ctDNA analysis in the clinic.

Improving prediction performance of colon cancer prognosis based on the integration of clinical and multi-omics data

Background

Colon cancer is common worldwide and is the leading cause of cancer-related death. Multiple levels of omics data are available due to the development of sequencing technologies. In this study, we proposed an integrative prognostic model for colon cancer based on the integration of clinical and multi-omics data.

Methods

In total, 344 patients were included in this study. Clinical, gene expression, DNA methylation and miRNA expression data were retrieved from The Cancer Genome Atlas (TCGA). To accommodate the high dimensionality of omics data, unsupervised clustering was used as dimension reduction method. The bias-corrected Harrell’s concordance index was used to verify which clustering result provided the best prognostic performance. Finally, we proposed a prognostic prediction model based on the integration of clinical data and multi-omics data. Uno’s concordance index with cross-validation was used to compare the discriminative performance of the prognostic model constructed with different covariates.

Results

Combinations of clinical and multi-omics data can improve prognostic performance, as shown by the increase of the bias-corrected Harrell’s concordance of the prognostic model from 0.7424 (clinical features only) to 0.7604 (clinical features and three types of omics features). Additionally, 2-year, 3-year and 5-year Uno’s concordance statistics increased from 0.7329, 0.7043, and 0.7002 (clinical features only) to 0.7639, 0.7474 and 0.7597 (clinical features and three types of omics features), respectively.

Conclusion

In conclusion, this study successfully combined clinical and multi-omics data for better prediction of colon cancer prognosis.

Development of a Precision Medicine Workflow in Hematological Cancers, Aalborg University Hospital, Denmark

Within recent years, many precision cancer medicine initiatives have been developed. Most of these have focused on solid cancers, while the potential of precision medicine for patients with hematological malignancies, especially in the relapse situation, are less elucidated. Here, we present a demographic unbiased and observational prospective study at Aalborg University Hospital Denmark, referral site for 10% of the Danish population. We developed a hematological precision medicine workflow based on sequencing analysis of whole exome tumor DNA and RNA. All steps involved are outlined in detail, illustrating how the developed workflow can provide relevant molecular information to multidisciplinary teams. A group of 174 hematological patients with progressive disease or relapse was included in a non-interventional and population-based study, of which 92 patient samples were sequenced. Based on analysis of small nucleotide variants, copy number variants, and fusion transcripts, we found variants with potential and strong clinical relevance in 62% and 9.5% of the patients, respectively. The most frequently mutated genes in individual disease entities were in concordance with previous studies. We did not find tumor mutational burden or micro satellite instability to be informative in our hematologic patient cohort.